dc.contributor.advisor | Rahman, Rafeed | |
dc.contributor.author | Siddique, Ahmed Zarir | |
dc.contributor.author | Ali, Asif | |
dc.contributor.author | Arefin, Mohammad Sultanul | |
dc.date.accessioned | 2025-01-15T04:35:40Z | |
dc.date.available | 2025-01-15T04:35:40Z | |
dc.date.copyright | ©2024 | |
dc.date.issued | 2024-10 | |
dc.identifier.other | ID 20301409 | |
dc.identifier.other | ID 20201049 | |
dc.identifier.other | ID 20201138 | |
dc.identifier.uri | http://hdl.handle.net/10361/25169 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 28-29). | |
dc.description.abstract | Ever since artificial intelligence was discovered, numerous research endeavours have
concentrated on comprehending its significance inside the corporate environment
[13]. These days customers are more into the quality of services provided by organisations
[5]. The growing number of organizations resulted in an increase in
competition and customer retention has become a major factor for businesses, as
understanding its influence can aid companies to develop effective marketing strategies.
The purpose of this paper is to comprehensively be able to understand the
e-commerce dynamics and use relevant machine learning techniques to evaluate and
use the results for the prediction of customer loyalty. The paper discusses the analysis
of customer loyalty using various data mining techniques, such as decision tree,
SVM, random forest, and logistic regression etc. We constructed some simple ensemble
applications using the machine learning algorithms, with the dataset that
we received from a Bangladesh-based e-commerce business. In the end, the abovementioned
algorithms are all carried out and the result demonstrates which model
is best for retaining customer loyalty. | en_US |
dc.description.statementofresponsibility | Ahmed Zarir Siddique | |
dc.description.statementofresponsibility | Asif Ali | |
dc.description.statementofresponsibility | Mohammad Sultanul Arefin | |
dc.format.extent | 38 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
dc.subject | E-commerce | en_US |
dc.subject | Customer relationship management | en_US |
dc.subject | Customer loyalty | en_US |
dc.subject | Machine learning | |
dc.subject.lcsh | Artificial intelligence. | |
dc.subject.lcsh | Electronic commerce--Databases. | |
dc.subject.lcsh | Data mining. | |
dc.title | Predictive models for customer retention in Bangladesh: enabling proactive strategies | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B.Sc. in Computer Science | |